November 2017 CIS Magizing cover

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). Additionally, CIM serves as a media of communications between the governing body and its membership of IEEE/CIS. Authors are encouraged to submit papers on applications oriented developments, successful industrial implementations, design tools, technology reviews, computational intelligence education, and applied research.

Contributions should contain novel and previously unpublished material. The novelty will usually lie in original concepts, results, techniques, observations, hardware/software implementations, or applications, but may also provide syntheses or new insights into previously reported research. Surveys and expository submissions are also welcome. In general, material which has been previously copyrighted, published or accepted for publication will not be considered for publication; however, prior preliminary or abbreviated publication of the material shall not preclude publication in this journal.

Impact Score

imapct scores

Journal Citation Metrics Journal Citation Metrics such as Impact Factor, Eigenfactor Score™ and Article Influence Score™ are available where applicable. Each year, Journal Citation Reports© (JCR) from Thomson Reuters examines the influence and impact of scholarly research journals. JCR reveals the relationship between citing and cited journals, offering a systematic, objective means to evaluate the world's leading journals.
Find out more about IEEE Journal Rankings.


Call for Special Issues

Featured Paper

Selected article from IEEE Computational Intelligence Magazine

stock image dialogue bubblesAn Evolutionary Strategy For Concept-Based Multi-Domain Sentiment Analysis

Inferencing the sentiment expressed within a document is still a challenging task, especially when it is necessary to consider the domain dimension. In order to improve inference algorithm effectiveness, one of the main challenges is to learn polarity values representing the concept-domain pair. In this paper, an approach which relies on evolutionary algorithms and exploiting semantic relationships for estimating domain-dependent polarities of opinion concepts is presented. The SenticNet resource is used as a starting point for extracting both concepts and common-sense expression relevant to the sentiment analysis topic. Subsequently, the creation of semantic relations is performed by exploiting the alignments between SenticNet and WordNet. Finally, an evolutionary strategy has been implemented for learning the polarity values of concept-domain pairs. The approach has been validated by following the Dranziera protocol and obtained results demonstrated the suitability of the proposed solution.

IEEE Computational Intelligence Magazine, May 2019